Robotics Commons^™

An Analysis Of Simultaneous Localization And Mapping (Slam) Algorithms, Megan R. Naminski

Mathematics, Statistics, and Computer Science Honors Projects

This paper provides an introduction to two Simultaneous Localization and Mapping (SLAM) algorithms: EKF SLAM and Fast-SLAM. SLAM allows an autonomous robot to accurately map an unknown environment as well as locate itself within the environment. These algorithms work iteratively, by moving about the environment and extracting and observing various landmarks in the environment. EKF SLAM and Fast-SLAM solve the SLAM problem by using probabilities to control for errors in the robot's sensors. This paper provides a discussion of these two algorithms and compares their run times and the accuracy of the maps they produce.

A Computer Vision Application To Accurately Estimate Object Distance, Kayton B. Parekh

Mathematics, Statistics, and Computer Science Honors Projects

Scientists have been working to create robots that perform manual work for years. However, creating machines that can navigate themselves and respond to their environment has proven to be difficult. One integral task to such research is to estimate the position of objects in the robot's visual field.

In this project we examine an implementation of computer vision depth perception. Our application uses color-based object tracking combined with model-based pose estimation to estimate the depth of specific objects in the view of our Pioneer 2 and Power Wheels robots. We use the Camshift algorithm for color-based object tracking, which uses …